# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

import paddle
from model import ElectraForBinaryTokenClassification, ElectraForSPO

from paddlenlp.transformers import ElectraForSequenceClassification

NUM_CLASSES = {
    "CHIP-CDN-2C": 2,
    "CHIP-STS": 2,
    "CHIP-CTC": 44,
    "KUAKE-QQR": 3,
    "KUAKE-QTR": 4,
    "KUAKE-QIC": 11,
    "CMeEE": [33, 5],
    "CMeIE": 44,
}


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--train_dataset", required=True, type=str, help="The name of dataset used for training.")
    parser.add_argument(
        "--params_path",
        type=str,
        required=True,
        default="./checkpoint/",
        help="The path to model parameters to be loaded.",
    )
    parser.add_argument(
        "--output_path", type=str, default="./export", help="The path of model parameter in static graph to be saved."
    )
    args = parser.parse_args()
    return args


def main():
    args = parse_args()

    # Load the model parameters.
    if args.train_dataset not in NUM_CLASSES:
        raise ValueError(f"Please modify the code to fit {args.dataset}")

    if args.train_dataset == "CMeEE":
        model = ElectraForBinaryTokenClassification.from_pretrained(
            args.params_path, num_classes=NUM_CLASSES[args.train_dataset]
        )
    elif args.train_dataset == "CMeIE":
        model = ElectraForSPO.from_pretrained(args.params_path, num_classes=NUM_CLASSES[args.train_dataset])
    else:
        model = ElectraForSequenceClassification.from_pretrained(
            args.params_path, num_classes=NUM_CLASSES[args.train_dataset], activation="tanh"
        )

    model.eval()

    # Convert to static graph with specific input description:
    # input_ids, token_type_ids
    input_spec = [
        paddle.static.InputSpec(shape=[None, None], dtype="int64"),
        paddle.static.InputSpec(shape=[None, None], dtype="int64"),
    ]
    model = paddle.jit.to_static(model, input_spec=input_spec)

    # Save in static graph model.
    save_path = os.path.join(args.output_path, "inference")
    paddle.jit.save(model, save_path)


if __name__ == "__main__":
    main()
